Join us for the Brooklyn Quant Experience (BQE) Lecture Series on Thursday, November 11th at 6 pm ET on Zoom.
“A Factor Model of Company Valuation”
Liuren Wu
Distinguished Professor of Finance
Baruch College
*Please note a meeting password is required for this event.
Meeting ID: 982 6827 3041
Password: BQELW1111
Join us for the Brooklyn Quant Experience (BQE) Lecture Series on Thursday, November 4th at 6 pm ET on Zoom. Only NYU students, faculty, and staff are allowed to attend in person. All other guests can attend synchronously via Zoom.
“Equilibrium Existence in a Limited Participation Economy”
Kim Weston
Assistant Professor of Mathematics
Rutgers University
*Please note a meeting password is required for this event.
Meeting ID: 969 7285 1683
Password: BQEKW114
Join us for the Brooklyn Quant Experience (BQE) Lecture Series on Thursday, October 28th at 6 pm ET on Zoom. Only the NYU Community is allowed to attend in person until further notice. All other guests can attend synchronously via Zoom.
Federico Bandi
James Carey Endowed Professor
Professor of Finance
Carey Business School
Johns Hopkins University
*Please note a meeting password is required for this event.
Meeting ID: 938 9750 3169
Password: BQEFB1028
Join us for the Brooklyn Quant Experience (BQE) Lecture Series on Thursday, October 21st at 6 pm ET on Zoom. Only the NYU Community is allowed to attend in person until further notice. All other guests can attend synchronously via Zoom.
“Arbitrage-Based Derivative Pricing without Stochastic Calculus”
David Shimko
Industry Full Professor
NYU Tandon FRE
*Please note a meeting password is required for this event.
Meeting ID: 973 0109 4125
Password: BQEDS1021
Abstract
In the famous Black-Scholes-Merton model, continuous arbitrage in a frictionless environment leads to a well-known arbitrage-based pricing relationship between a single European call option and an underlying stock. In our discrete-time model, we use static arbitrage relationships across all options to find the same result. Our analysis also lays bare the impact of the powerful self-financing (SF) condition. While BSM requires the SF condition, we do not, leading to a stronger result. Additionally, we find that derivatives can be valued in the static CAPM provided a no-static-arbitrage constraint is included in the assumption set, resolving a 40-year-old dilemma. Finally, we show that option pricing could have been rigorously developed before the CAPM was created, using high school mathematics.
Featuring Machine Learning experts from Cornell, Citi, and more…
You and your colleagues are invited to attend the Cornell – Citi Financial Data Science Webinars. Through the online talks in Fall 2021, we are excited to collaborate with Citi in highlighting machine learning applications in finance.
All webinars are from 5:00 pm to 6:00 pm ET.
This webinar is free and open to all guests. Registration is required (Please RSVP here). You will receive the webinar link and dial-in info upon registration (the confirmation email will come from
no-reply@zoom.us)
Date: Tuesday, October 26th, 2021
Time: 5:00pm – 6:00pm ET
Speaker: Zihao Zhang | Oxford-Man Institute
Title: Deep Learning for Market by Order Data
Abstract: Market by order (MBO) data – a detailed feed of individual trade instructions for a given stock on an exchange – is arguably one of the most granular sources of microstructure information. While limit order books (LOBs) are implicitly derived from it, MBO data is largely neglected by current academic literature which focuses primarily on LOB modelling. In this paper, we demonstrate the utility of MBO data for forecasting high-frequency price movements, providing an orthogonal source of information to LOB snapshots and expanding the universe of alpha discovery. We provide the first predictive analysis on MBO data by carefully introducing the data structure and presenting a specific normalisation scheme to consider level information in order books and to allow model training with multiple instruments. Through forecasting experiments using deep neural networks, we show that while MBO-driven and LOB-driven models individually provide similar performance, ensembles of the two can lead to improvements in forecasting accuracy – indicating that MBO data is additive to LOB-based features.
Speaker Bio: Dr. Zihao Zhang is a postdoctoral researcher at the Oxford-Man Institute and Machine Learning Research Group at the University of Oxford. Zihao’s research focuses on quantitative finance with a special emphasis on applying deep learning models to financial time series modelling. Zihao’s current projects include portfolio optimization and reinforcement learning. Zihao holds a Ph.D. and MSc in Applied Statistics from the University of Oxford and a BSc in Economics and Statistics from University College London.
We hope to see you online!
The Cornell-Citi Team
**Please excuse any duplication of this announcement
If you are interested in our past seminars, you are welcome to subscribe to our YouTube Channel and watch our videos!
Past Events
Oct. 1-3, 2021
5th Eastern Conference on Mathematical Finance (ECMF)
Oct. 5th, 2021
Speaker: Alok Dutt (Citigroup)
Title of Presentation: The Top Challenges for a Financial Data Scientist (And How to Overcome Them)
Upcoming CFEM Events
Nov. 16th, 2021
Speaker: Laura Leal (Princeton)
Title of Presentation: TBD
Join us for the Brooklyn Quant Experience (BQE) Lecture Series on Thursday, October 14th at 6 pm ET on Zoom. Only the NYU Community is allowed to attend in person until further notice. All other guests can attend synchronously via Zoom.
“Optionality as a Binary Operation”
Peter Carr
Department Chair
Professor
NYU Tandon FRE
*Please note a meeting password is required for this event.
Meeting ID: 976 1104 7411
Password: BQEPC1014
Join us for the Brooklyn Quant Experience (BQE) Lecture Series on Thursday, September 30th at 6 pm ET on Zoom. Only the NYU Community is allowed to attend in person until further notice. All other guests can attend synchronously via Zoom.
Charles Fishkin
Adjunct Professor
Financial Engineering
Baruch College
*Please note a meeting password is required for this event.
Meeting ID: 969 2829 7700
Password: BQECF930
Featuring Machine Learning experts from Cornell, Citi, and more…
You and your colleagues are invited to attend the Cornell – Citi Financial Data Science Webinars. Through the online talks in Fall 2021, we are excited to collaborate with Citi in highlighting machine learning applications in finance.
All webinars are from 5:00 pm to 6:00 pm ET.
This webinar is free and open to all guests. Registration is required (RSVP). You will receive the webinar link and dial-in info upon registration (the confirmation email will come from
no-reply@zoom.us)
Date: Tuesday, October 5th, 2021
Time: 5:00 pm – 6:00 pm ET
Speaker: Alok Dutt | Citigroup
Title: “The Top Challenges for a Financial Data Scientist (and How to Overcome Them)
Abstract
The confluence of data availability, business needs, technological advances, and education have created great opportunities for data science and data scientists in the financial industry. However, after plunging into a project, a data scientist often encounters several real-world obstacles that they may not be prepared for. Fortunately, most of the issues fall under a small number of categories and can be handled with some prior knowledge and information. We will describe some of the major challenges to achieving successful results, and some techniques and principles to accelerate the practical learning curve to become an effective data scientist.
Speaker Bio
Alok Dutt is Head of Analytics in the Markets Quantitative Analysis division at Citigroup. He is responsible for a variety of advanced projects in automation, trading algorithms, data science, and data engineering across multiple business lines and asset classes. In his role at Citi, he applies data and quantitative techniques to automate business processes including research, modeling, trading, simulation, and visualization. Alok has extensive experience in several broad areas of quantitative finance, including exotics modeling, algorithmic trading, and market-making. Before Citi, he developed the trading models and algorithms for a new automated options market-making group at Morgan Stanley that was the subject of an HBS case study on disruptive innovation in a large organization. Prior to that, Alok was an exotics modeler and trader and established the first multi-asset hybrid trading desk at Bank of America. Alok has a Ph.D. in Computer Science from Yale University and a BA in Mathematics from Cambridge University.
We hope to see you online!
The Cornell-Citi Team
**Please excuse any duplication of this announcement
If you are interested in our past seminars, you are welcome to subscribe to our YouTube Channel and watch our videos!
Upcoming CFEM Events
Oct. 26th, 2021
Speaker: Zihao Zhang (Oxford-Man Institute)
Title of Presentation: TBD
Nov. 16th, 2021
Speaker: Laura Leal (Princeton)
Title of Presentation: TBD
The Brooklyn Quant Experience (BQE) Lecture Series will return for the Fall 2021 semester on Thursday, September 23rd at 6 pm ET on Zoom. Only the NYU Community is allowed to attend in person until further notice. All other guests can attend synchronously via Zoom.
“What are the Technical Errors on Covid?”
Nassim Nicholas Taleb
Retired Distinguished Professor
Department of Finance and Risk Engineering
NYU Tandon School of Engineering
*Please note a meeting password is required for this event.
Meeting ID: 971 7885 1816
Password: BQENT923
Featuring Machine Learning experts from Cornell, Citi, and more…
You and your colleagues are invited to attend the Cornell – Citi Financial Data Science Webinars. Through the online talks in Spring 2021, we are excited to collaborate with Citi in highlighting machine learning applications in finance.
All webinars are from 5:00 pm to 6:00 pm EDT.
This webinar is free and open to all guests. Registration is required (RSVP). You will receive the webinar link and dial-in info upon registration (the confirmation email will come from
no-reply@zoom.us)
Date: Tuesday, May 11th, 2021
Time: 5:00 pm – 6:00 pm EDT
Speaker: Nicholas Venuti | Morgan Stanley
Title: “Advances in Sequential Deep Learning”
Abstract
Sequential data serves as the basis for many real-world applications such as machine translation, voice-to-text conversion, and motion tracking. As the order and context of past datapoints are needed for future prediction, these datasets must be modelled temporally either by constructing features or utilizing recursive models.
Many state-of-the-art solutions include recurrent neural networks (RNNs), as these methods are able to exploit both techniques by capturing the time-based nature of these systems while leveraging the expressiveness of deep learning architectures. While RNN variants such as gated recurrent units (GRUs) and long-short term memory (LSTM) networks have dominated sequential deep learning, recent studies have found that temporal convolutional networks (TCNs) can match or exceed these networks in prediction performance while greatly reducing the training time of the models.
In this talk, we will provide a general overview of four common architectures: vanilla RNNs, GRUs, LSTMs, and TCNs. We will compare the model stability, memory requirements, and training times of each. Lastly, we will review the performance of these architectures on a variety of benchmark image, text, and audio datasets.
Program Agenda:
Speaker Bio
Nicholas Venuti is a Machine Learning Research Scientist in Morgan Stanley’s Machine Learning Center of Excellence, whose main research focus is deep learning architectures for time-series predictions. After obtaining his Bachelor of Science in Biomolecular Chemical Engineering at North Carolina State University, Nicholas began his career working in data analytics at an environmental consultancy. Afterwards, he obtained his Masters of Data Science at the University of Virginia, where his thesis studied using NLP to identify semantic shifts in religious and political texts as an early indicator for extremist views.
“Lightning Talk” Info:
CFEM alumnus Vineel Yellapantula will discuss his summer project at AbleMarkets under Prof. Irene Aldridge, “Quantifying Sentiment in SEC Filings.” By utilizing Natural Language Processing techniques and the BERT model, he explored how text present in the MD&A section of 10-K and 10-Q filings affect the performance of the stock. He also tested the efficacy of multiple factors derived from these texts using a long-short market-neutral trading strategy.
Vineel Yellapantula (MFE Cornell ’20, MSc Mathematics BITS Pilani ’18) is a Decision Analytics Associate at ZS Associates.
We hope to see you online!
The Cornell-Citi Team
**Please excuse any duplication of this announcement
If you are interested in our past seminars, you are welcome to subscribe to our YouTube Channel and watch our videos!
Past CFEM Events
February 16th, 2021
Speaker: Charles-Albert Lehalle (Capital Fund Management)
Title of Presentation: “An Attempt to Understand Natural Language Processing and Illustration on a Financial Dataset”
March 9th, 2021
Speaker: Bruno Dupire (Bloomberg)
Title of Presentation: “Some Applications of Machine Learning in Finance”
April 13th, 2021
Speaker: Peter Carr (NYU) and Lorenzo Torricelli (University of Parma)
Title of Presentation: “Stoptions” and “Additive Logistic Processes in Option Pricing” (PDFs available upon request)